Ecological drivers of flower–leaf sequences: aridity and proxies for pollinator attraction select for flowering‐first in the American plums
Bibliographic record
Abstract
Across temperate forests, many tree species produce flowers before their leaves emerge. This flower-leaf phenological sequence, known as hysteranthy, is generally described as an adaptation for wind pollination. However, this explanation does not address why hysteranthy is also common in biotically pollinated taxa. We quantified flower-leaf sequence variation in the American plums (Prunus, subg. Prunus sect. Prunocerasus), a clade of insect-pollinated trees, using herbaria specimens and Bayesian hierarchical modeling. We tested two common, but rarely interrogated hypotheses - that hysteranthy confers aridity tolerance and/or pollinator visibility - by modeling the associations between hysteranthy and related traits. To understand how these phenology-trait associations were sensitive to taxonomic scale and flower-leaf sequence classification, we then extended these analyses to all Prunus species in North America. Our findings across two taxonomic levels support the hypotheses that hysteranthy may help temporally partition hydraulic demand to reduce water stress and increase pollinator visibility - thereby reducing selective pressure on inflorescence size. Our results provide foundational insights into the evolution of flower-leaf sequences in the genus Prunus, with implications for understanding these patterns in biotically pollinated plants in general. Our approach suggests a path to advance these hypotheses to other clades, but teasing out drivers fully will require new experiments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".